1. Field of the Invention
The present invention relates to a method and device for recognizing the dangerousness of an object, and particularly relates to a method and device for recognizing the dangerousness of an object on the basis of an image captured by a stereo camera.
2. Description of the Related Art
Up to now, monitoring systems have been provided in many public areas such as railway stations, airports, and the like. The discovery and recognition of a dangerous object plays an very important role for establishing an effective monitoring system. This kind of ability of recognizing the dangerousness of an object (also called “object dangerousness”) may assist people to detect a hidden security risk in the public areas so as to avoid being wounded, etc.
A method of determining the object dangerousness on the basis of the shape of an object in an image captured by a camera installed in the monitoring system has been proposed. However, this kind of method may easily receive the influence of the change of a view angle, an obstacle, etc., so it may not effectively classify the object dangerousness, and sometimes may not obtain a representative image of the object.
Furthermore a method of determining the object dangerousness by utilizing a detection device different from a well-used camera installed in the monitoring system has been proposed. The detection device may use infrared rays, X-ray computed tomography, or microwave imaging to carry out its detection. However, although this kind of method may acquire data by which some features of the object may be stably expressed, its cost is relatively high, and sometimes it needs an additional device such as a scanner. This may result in inconvenience especially when being used in the public areas.
On the other hand, in the conventional methods of determining the object dangerousness, it is also necessary to use a specific dangerous object model which is predetermined or obtained in advance by conducting machine learning, so as to determine, by carrying out model matching, whether the object is dangerous. This may result in low determination accuracy if the object is significantly different from the specific dangerous object model.